class_docstr: Stack ensemble meta-model which functions identically to :class:`BaggedEnsembleModel` with the additional capability to leverage base models.
  
  By specifying base models during init, stacker models can use the base model predictions as features during training and inference.
  
  This property allows for significantly improved model quality in many situations compared to non-stacking alternatives.
  
  Stacker models can act as base models to other stacker models, enabling multi-layer stack ensembling.
code: def __init__(
      self,
      base_model_names=None,
      base_models_dict=None,
      base_model_paths_dict=None,
      base_model_types_dict=None,
      base_model_types_inner_dict=None,
      base_model_performances_dict=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if base_model_names is None:
          base_model_names = []
      if base_models_dict is None:
          base_models_dict = {}
      if base_model_paths_dict is None:
          base_model_paths_dict = {}
      if base_model_types_dict is None:
          base_model_types_dict = {}
      self.base_model_names = base_model_names
      self.base_models_dict: Dict[
          str, AbstractModel
      ] = base_models_dict  # String name -> Model objects
      self.base_model_paths_dict = {
          key: os.path.relpath(val, self.path)
          for key, val in base_model_paths_dict.items()
      }
      self.base_model_types_dict = base_model_types_dict
  
      # TODO: Consider deleting these variables after initialization
      self._base_model_performances_dict = base_model_performances_dict
      self._base_model_types_inner_dict = base_model_types_inner_dict
  
imports: import copy
  import logging
  import os
  import time
  from collections import defaultdict
  from typing import Dict
  import numpy
  import pandas
  from autogluon.common.features.feature_metadata import FeatureMetadata
  from autogluon.common.features.types import R_FLOAT, S_STACK
  from autogluon.common.utils.path_converter import PathConverter
  from ...constants import MULTICLASS, QUANTILE, SOFTCLASS
  from ..abstract.abstract_model import AbstractModel
  from .bagged_ensemble_model import BaggedEnsembleModel
label_desc: Use image node_id 1 to create a new StackerEnsembleModel object from inherited base classes: BaggedEnsembleModel with example: obj = StackerEnsembleModel(base_model_names, base_models_dict, base_model_paths_dict, base_model_types_dict, base_model_types_inner_dict, base_model_performances_dict)